docstr: sentence - array of words
docstr_tok: ["sentence","-","array","of","words"]
code: def resolve_sentence(self, sentence):
      """
      sentence - array of words
      """
      embeddings = list(
          [self.get_embeddings(word) for word in sentence]
      )
      is_word_start = [True] * len(sentence)
  
      return TokenEmbeddings.create_sentence(
          sentence, is_word_start, embeddings
      )
  
imports: import shutil
  import numpy
  import plyvel
  import os.path
  import sys
  from bert.modeling import *
  from bert.tokenization import *
  import json
  import os.path
  import numpy
label_desc: Use image node_id 6 for calling the EmbeddingsDbResolver obj's underlying member method code with example usage: obj.resolve_sentence(sentence) and returns: TokenEmbeddings